# AIRx Director, Computational & AI Biologics Design Lead — Takeda

## Posting metadata

- Canonical source: https://jobs.takeda.com/job/-/-/1113/96234442112
- Provider: Takeda Careers
- Requisition/reference: 96234442112
- Company: Takeda
- Title: AIRx Director, Computational & AI Biologics Design Lead
- Location: Boston, Massachusetts
- Work model: Hybrid
- Compensation: $177,000-$278,080 annually
- Applicants: Not disclosed
- Posting time: Not disclosed in the retrieved page
- Generated: August 31, 2026 at 6:48 PM EDT

## Eligibility verification

The hybrid Boston work model and $278,080 compensation ceiling satisfy Keith’s location and compensation gates. Applicant count and travel were not disclosed and are treated as caveats rather than automatic failures.

## Positioning track

Senior technical/scientific individual contributor and computational biologics design lead.

## Fit outcome

**FAIL — hard scientific-domain and hands-on technical gaps.**

Keith has relevant foundations in early bioinformatics, genomics, AI/ML, platform engineering, and enterprise AI leadership. The role, however, requires current and deep hands-on expertise in computational biologics and large-molecule drug discovery. Required evidence includes antibody/protein sequence-structure-function modeling, generative and predictive AI specifically applied to biologics, molecular simulation and structural analysis, developability prediction, PK/PD and TMDD concepts, species cross-reactivity, multispecific antibody formats, and deployed ML models that materially advanced biologics discovery programs. Keith’s source materials do not establish this specialization or the requested 10+ years of computational impact in biologics drug discovery.

This is not a wording or keyword gap; it is a hard domain-experience gap. No resume, cover letter, or LinkedIn outreach snippet was generated.

## Core responsibilities and requirements

- Lead computational and AI-driven design of biologics and large-molecule therapeutics.
- Develop and apply sequence-, structure-, and property-based models to antibody and protein design.
- Integrate generative AI, predictive ML, molecular simulation, structural biology, and developability assessment into discovery workflows.
- Partner with experimental and multidisciplinary drug-discovery teams to prioritize designs and advance programs.
- Build or deploy computational tools and models that produce measurable impact across biologics discovery portfolios.
- Apply understanding of PK/PD, target-mediated drug disposition, species cross-reactivity, and multispecific antibody formats.
- Provide technical leadership, scientific direction, and cross-functional influence.
- Hold a PhD or equivalent relevant scientific training and demonstrate extensive industry drug-discovery experience.

## Direct-match strengths

- Early career bioinformatics and computational genomics experience in biotechnology.
- Long-standing AI/ML, data-platform, cloud, and software-product expertise.
- Experience translating advanced technology into production systems and business workflows.
- Executive and technical leadership across multidisciplinary teams.

## Material gaps and caveats

1. No source-backed record of antibody or protein engineering and design.
2. No source-backed record of generative/predictive models for biologics discovery.
3. No source-backed depth in molecular simulation, structural biology, developability prediction, PK/PD, TMDD, cross-reactivity, or multispecific formats.
4. No demonstrated 10+ years of computational impact within large-molecule drug-discovery programs.
5. Applicant count and travel are not disclosed.

## Artifact metadata

- JD capture: Generated for auditability.
- Resume: Not generated because of hard domain gaps.
- Cover letter: Not generated.
- Google Drive: Not used.